A control plane for deploying, evaluating, monitoring, and improving production ML? systems across backend data pipelines.
Added May 29, 2026
Companies are hiring engineers to build and maintain scalable ML? infrastructure because production AI systems require more than model code. Teams struggle to connect data pipelines, model deployments, evaluations, monitoring, and feedback loops into reliable systems aligned to business outcomes.
The product is a SaaS? MLOps control plane that plugs into existing data and backend systems to orchestrate ML? deployments, evaluations, monitoring, and pipeline health checks. It provides unified workflows for data ingestion, model evaluation, production rollout, accuracy measurement, and feedback-loop tracking so engineering teams can operate AI systems at scale.
Multiple companies are explicitly hiring for production ML? infrastructure, evaluation pipelines, monitoring systems, and AI model orchestration. This suggests AI teams are moving from experimentation into operational reliability and need tooling that reduces custom platform work.
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• Architect scalable machine learning systems and partner with software engineers to integrate AI components into end-to-end platforms. • Own model performance and system reliability by driving best practices in MLOps, deployment, monitoring, and continuous improvement.
As Engineering Manager, you'll lead a high-performing, inclusive team of ML and platform engineers, responsible for turning research and models into production-grade ML infrastructure that runs at scale. You'll balance predictable delivery with a relentless focus on system health, taking the lead on MLOps maturity, observability and the operational realities of running AI systems in production, not just shipping features.
Architect, deploy, and maintain core ML systems powering long-horizon AI features. Manage the full ML lifecycle, including data pipelines, model training, evaluation, inference, and continuous deployment.
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